1. Field of the Invention
The present invention related generally to vector data, map, and imagery systems, and in particular to a system for automatically conflating road vector data, street maps, and orthoimagery.
2. Discussion of the Related Art
Over the years, the amount of available geospatial information has dramatically increased, making access possible to various types of spatial data. By integrating diverse spatial datasets, queries are now possible that could have not been previously answered given any of these datasets in isolation. However, accurately integrating different types of geospatial data remains a challenging task because diverse geospatial data have different projections and different accuracy levels, for example. Many types of conflation techniques only provide vector-vector data integration, or require human intervention to accomplish vector-raster or raster-raster data integration.
Conflation refers to the process used to integrate or align different geospatial datasets. The conflation process in accordance with an embodiment may be divided into the following general subtasks: (1) feature matching: find a set of conjugate point pairs, termed control point pairs, in two datasets; (2) match checking: detect inaccurate control point pairs from a set of control point pairs; and (3) alignment: use of accurate control point pairs to align the geospatial objects (such as points or lines) in both datasets using triangulation and rubber-sheeting techniques, for example.
There have been considerable efforts to automatically or semi-automatically accomplish vector to vector conflation. Some of the proposed approaches focus on handling the integration of two road networks. Other proposed strategies utilize a relational matching approach to find matched spatial objects based on the similarity of these spatial objects at the geometry level (e.g., node to node matching based on distance), as well as the relationship between the elements in a dataset. This strategy investigated the “similarity” of spatial objects based on statistical information derived from a random sample of the vector datasets to be integrated. However, this approach requires human intervention to perform an initial affine transformation between datasets.
There have been further attempts to automatically or semi-automatically accomplish raster to raster conflation. For instance, many commercial geographic information systems (GIS) products provide a certain level of functionality relating to the conflation of imagery and maps (i.e., raster to raster registration) using different types of transformation methods. However, these products do not provide automatic conflation, so users are required to manually select control points for conflation. In general, manual conflation techniques are labor intensive, and are subject to undesirable user errors.